The converter transformer is a core component in ultra-high voltage direct current(UHVDC) transmission systems

and its condition monitoring is crucial to ensuring the stable operation of the power grid.To address the challenges of poor fault feature extraction and limited diagnostic accuracy in converter transformers

this paper proposes a fault diagnosis method for converter transformers based on time-frequency maps and a parallel CNN-LSTM architecture.First

one-dimensional time-series signals are converted into time-frequency maps using continuous wavelet transform(CWT).〖JP+1〗Subsequently

both the one-dimensional time-series signals and two-dimensional time-frequency images are simulta-neously input into a dual-branch parallel convolutional neural network(CNN) for feature extraction.Next

the extracted features from both modalities are subsequently concatenated and fused

with important features further enhanced using a squeeze-and-excitation(SE) channel attention mechanism.Finally

a long short-term memory(LSTM) network is used to capture temporal feature information for fault diagnosis.Experimental results on real-world data from converter stations show that the proposed method significantly outperforms existing advanced methods in terms of diagnostic accuracy

demonstrating improved feature representation and superior fault identification capabilities.